114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 | @dataclass(init=False) class HuggingFaceModel(Model): """A model that uses Hugging Face Inference Providers. Internally, this uses the [HF Python client](https://github.com/huggingface/huggingface_hub) to interact with the API. Apart from `__init__`, all methods are private or match those of the base class. """ client: AsyncInferenceClient = field(repr=False) _model_name: str = field(repr=False) _provider: Provider[AsyncInferenceClient] = field(repr=False) def __init__( self, model_name: str, *, provider: Literal['huggingface'] | Provider[AsyncInferenceClient] = 'huggingface', profile: ModelProfileSpec | None = None, settings: ModelSettings | None = None, ): """Initialize a Hugging Face model. Args: model_name: The name of the Model to use. You can browse available models [here](https://huggingface.co/models?pipeline_tag=text-generation&inference_provider=all&sort=trending). provider: The provider to use for Hugging Face Inference Providers. Can be either the string 'huggingface' or an instance of `Provider[AsyncInferenceClient]`. If not provided, the other parameters will be used. profile: The model profile to use. Defaults to a profile picked by the provider based on the model name. settings: Model-specific settings that will be used as defaults for this model. """ self._model_name = model_name if isinstance(provider, str): provider = infer_provider(provider) self._provider = provider self.client = provider.client super().__init__(settings=settings, profile=profile or provider.model_profile) @property def base_url(self) -> str: """The base URL of the provider.""" return self._provider.base_url @property def model_name(self) -> HuggingFaceModelName: """The model name.""" return self._model_name @property def system(self) -> str: """The system / model provider.""" return self._provider.name async def request( self, messages: list[ModelMessage], model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters, ) -> ModelResponse: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) response = await self._completions_create( messages, False, cast(HuggingFaceModelSettings, model_settings or {}), model_request_parameters ) model_response = self._process_response(response) return model_response @asynccontextmanager async def request_stream( self, messages: list[ModelMessage], model_settings: ModelSettings | None, model_request_parameters: ModelRequestParameters, run_context: RunContext[Any] | None = None, ) -> AsyncIterator[StreamedResponse]: check_allow_model_requests() model_settings, model_request_parameters = self.prepare_request( model_settings, model_request_parameters, ) response = await self._completions_create( messages, True, cast(HuggingFaceModelSettings, model_settings or {}), model_request_parameters ) yield await self._process_streamed_response(response, model_request_parameters) @overload async def _completions_create( self, messages: list[ModelMessage], stream: Literal[True], model_settings: HuggingFaceModelSettings, model_request_parameters: ModelRequestParameters, ) -> AsyncIterable[ChatCompletionStreamOutput]: ... @overload async def _completions_create( self, messages: list[ModelMessage], stream: Literal[False], model_settings: HuggingFaceModelSettings, model_request_parameters: ModelRequestParameters, ) -> ChatCompletionOutput: ... async def _completions_create( self, messages: list[ModelMessage], stream: bool, model_settings: HuggingFaceModelSettings, model_request_parameters: ModelRequestParameters, ) -> ChatCompletionOutput | AsyncIterable[ChatCompletionStreamOutput]: tools = self._get_tools(model_request_parameters) if not tools: tool_choice: Literal['none', 'required', 'auto'] | None = None elif not model_request_parameters.allow_text_output: tool_choice = 'required' else: tool_choice = 'auto' if model_request_parameters.builtin_tools: raise UserError('HuggingFace does not support built-in tools') hf_messages = await self._map_messages(messages, model_request_parameters) try: return await self.client.chat.completions.create( # type: ignore model=self._model_name, messages=hf_messages, # type: ignore tools=tools, tool_choice=tool_choice or None, stream=stream, stop=model_settings.get('stop_sequences', None), temperature=model_settings.get('temperature', None), top_p=model_settings.get('top_p', None), seed=model_settings.get('seed', None), presence_penalty=model_settings.get('presence_penalty', None), frequency_penalty=model_settings.get('frequency_penalty', None), logit_bias=model_settings.get('logit_bias', None), # type: ignore logprobs=model_settings.get('logprobs', None), top_logprobs=model_settings.get('top_logprobs', None), extra_body=model_settings.get('extra_body'), # type: ignore ) except aiohttp.ClientResponseError as e: raise ModelHTTPError( status_code=e.status, model_name=self.model_name, body=e.message, ) from e except HfHubHTTPError as e: raise ModelHTTPError( status_code=e.response.status_code, model_name=self.model_name, body=e.response.content, ) from e def _process_response(self, response: ChatCompletionOutput) -> ModelResponse: """Process a non-streamed response, and prepare a message to return.""" if response.created: timestamp = datetime.fromtimestamp(response.created, tz=timezone.utc) else: timestamp = _now_utc() choice = response.choices[0] content = choice.message.content tool_calls = choice.message.tool_calls items: list[ModelResponsePart] = [] if content: items.extend(split_content_into_text_and_thinking(content, self.profile.thinking_tags)) if tool_calls is not None: for c in tool_calls: items.append(ToolCallPart(c.function.name, c.function.arguments, tool_call_id=c.id)) raw_finish_reason = choice.finish_reason provider_details = {'finish_reason': raw_finish_reason} finish_reason = _FINISH_REASON_MAP.get(cast(TextGenerationOutputFinishReason, raw_finish_reason), None) return ModelResponse( parts=items, usage=_map_usage(response), model_name=response.model, timestamp=timestamp, provider_response_id=response.id, provider_name=self._provider.name, provider_url=self.base_url, finish_reason=finish_reason, provider_details=provider_details, ) async def _process_streamed_response( self, response: AsyncIterable[ChatCompletionStreamOutput], model_request_parameters: ModelRequestParameters ) -> StreamedResponse: """Process a streamed response, and prepare a streaming response to return.""" peekable_response = _utils.PeekableAsyncStream(response) first_chunk = await peekable_response.peek() if isinstance(first_chunk, _utils.Unset): raise UnexpectedModelBehavior( # pragma: no cover 'Streamed response ended without content or tool calls' ) return HuggingFaceStreamedResponse( model_request_parameters=model_request_parameters, _model_name=first_chunk.model, _model_profile=self.profile, _response=peekable_response, _timestamp=datetime.fromtimestamp(first_chunk.created, tz=timezone.utc), _provider_name=self._provider.name, _provider_url=self.base_url, ) def _get_tools(self, model_request_parameters: ModelRequestParameters) -> list[ChatCompletionInputTool]: return [self._map_tool_definition(r) for r in model_request_parameters.tool_defs.values()] async def _map_messages( self, messages: list[ModelMessage], model_request_parameters: ModelRequestParameters ) -> list[ChatCompletionInputMessage | ChatCompletionOutputMessage]: """Just maps a `pydantic_ai.Message` to a `huggingface_hub.ChatCompletionInputMessage`.""" hf_messages: list[ChatCompletionInputMessage | ChatCompletionOutputMessage] = [] for message in messages: if isinstance(message, ModelRequest): async for item in self._map_user_message(message): hf_messages.append(item) elif isinstance(message, ModelResponse): texts: list[str] = [] tool_calls: list[ChatCompletionInputToolCall] = [] for item in message.parts: if isinstance(item, TextPart): texts.append(item.content) elif isinstance(item, ToolCallPart): tool_calls.append(self._map_tool_call(item)) elif isinstance(item, ThinkingPart): start_tag, end_tag = self.profile.thinking_tags texts.append('\n'.join([start_tag, item.content, end_tag])) elif isinstance(item, BuiltinToolCallPart | BuiltinToolReturnPart): # pragma: no cover # This is currently never returned from huggingface pass elif isinstance(item, FilePart): # pragma: no cover # Files generated by models are not sent back to models that don't themselves generate files. pass else: assert_never(item) message_param = ChatCompletionInputMessage(role='assistant') # type: ignore if texts: # Note: model responses from this model should only have one text item, so the following # shouldn't merge multiple texts into one unless you switch models between runs: message_param['content'] = '\n\n'.join(texts) if tool_calls: message_param['tool_calls'] = tool_calls hf_messages.append(message_param) else: assert_never(message) if instructions := self._get_instructions(messages, model_request_parameters): hf_messages.insert(0, ChatCompletionInputMessage(content=instructions, role='system')) # type: ignore return hf_messages @staticmethod def _map_tool_call(t: ToolCallPart) -> ChatCompletionInputToolCall: return ChatCompletionInputToolCall.parse_obj_as_instance( # type: ignore { 'id': _guard_tool_call_id(t=t), 'type': 'function', 'function': { 'name': t.tool_name, 'arguments': t.args_as_json_str(), }, } ) @staticmethod def _map_tool_definition(f: ToolDefinition) -> ChatCompletionInputTool: tool_param: ChatCompletionInputTool = ChatCompletionInputTool.parse_obj_as_instance( # type: ignore { 'type': 'function', 'function': { 'name': f.name, 'description': f.description, 'parameters': f.parameters_json_schema, }, } ) return tool_param async def _map_user_message( self, message: ModelRequest ) -> AsyncIterable[ChatCompletionInputMessage | ChatCompletionOutputMessage]: for part in message.parts: if isinstance(part, SystemPromptPart): yield ChatCompletionInputMessage.parse_obj_as_instance({'role': 'system', 'content': part.content}) # type: ignore elif isinstance(part, UserPromptPart): yield await self._map_user_prompt(part) elif isinstance(part, ToolReturnPart): yield ChatCompletionOutputMessage.parse_obj_as_instance( # type: ignore { 'role': 'tool', 'tool_call_id': _guard_tool_call_id(t=part), 'content': part.model_response_str(), } ) elif isinstance(part, RetryPromptPart): if part.tool_name is None: yield ChatCompletionInputMessage.parse_obj_as_instance( # type: ignore {'role': 'user', 'content': part.model_response()} ) else: yield ChatCompletionInputMessage.parse_obj_as_instance( # type: ignore { 'role': 'tool', 'tool_call_id': _guard_tool_call_id(t=part), 'content': part.model_response(), } ) else: assert_never(part) @staticmethod async def _map_user_prompt(part: UserPromptPart) -> ChatCompletionInputMessage: content: str | list[ChatCompletionInputMessage] if isinstance(part.content, str): content = part.content else: content = [] for item in part.content: if isinstance(item, str): content.append(ChatCompletionInputMessageChunk(type='text', text=item)) # type: ignore elif isinstance(item, ImageUrl): url = ChatCompletionInputURL(url=item.url) # type: ignore content.append(ChatCompletionInputMessageChunk(type='image_url', image_url=url)) # type: ignore elif isinstance(item, BinaryContent): if item.is_image: url = ChatCompletionInputURL(url=item.data_uri) # type: ignore content.append(ChatCompletionInputMessageChunk(type='image_url', image_url=url)) # type: ignore else: # pragma: no cover raise RuntimeError(f'Unsupported binary content type: {item.media_type}') elif isinstance(item, AudioUrl): raise NotImplementedError('AudioUrl is not supported for Hugging Face') elif isinstance(item, DocumentUrl): raise NotImplementedError('DocumentUrl is not supported for Hugging Face') elif isinstance(item, VideoUrl): raise NotImplementedError('VideoUrl is not supported for Hugging Face') elif isinstance(item, CachePoint): # Hugging Face doesn't support prompt caching via CachePoint pass else: assert_never(item) return ChatCompletionInputMessage(role='user', content=content) # type: ignore
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